Learning and using generalized string patterns for information extraction

ABSTRACT

The present invention relates to extracting information from an information source. During extraction, strings in the information source are accessed. These strings in the information source are matched with generalized extraction patterns that include words and wildcards. The wildcards denote that at least one word in an individual string can be skipped in order to match the individual string to an individual generalized extraction pattern.

BACKGROUND OF THE INVENTION

The present invention relates to information extraction. In particular,the present invention relates to systems and methods for performinginformation extraction.

Many databases, web pages and documents exist that contain a largeamount of information. With such a large amount of existing information,many methods have been used in order to gather relevant informationpertaining to a particular subject. Information extraction refers to atechnique for extracting useful information from these informationsources. Generally, an information extraction system extractsinformation based on extraction patterns (or extraction rules).

Manually writing and developing reliable extraction patterns isdifficult and time consuming. As a result, many efforts have been madeto automatically learn extraction patterns from annotated examples. Insome automatic learning systems, natural language patterns are learnedby syntactically parsing sentences and acquiring sentential or phrasalpatterns from the parses. Another approach discovers patterns usingsyntactic and semantic constraints. However, these approaches aregenerally costly to develop. Another approach uses consecutive surfacestring patterns for extracting information on particular pairs ofinformation. These consecutive patterns only cover a small amount ofinformation to be extracted and thus do not provide sufficientgeneralization of a large amount of information for reliable extraction.

Many different methods have been devised to address the problemspresented above. A system and method for accurately and efficientlylearning patterns for use in information extraction would furtheraddress these and/or other problems to provide a more reliable, costeffective information extraction system.

SUMMARY OF THE INVENTION

The present invention relates to extracting information from aninformation source. During extraction, strings in the information sourceare accessed. These strings in the information source are matched withgeneralized extraction patterns that include words and wildcards. Thewildcards denote that at least one word in an individual string can beskipped in order to match the individual string to an individualgeneralized extraction pattern.

Another aspect of the present invention is a computer-readable mediumfor extracting information from an information source. The mediumincludes a data structure that has a set of generalized extractionpatterns including words and an indication of a position for at leastone optional word. The medium also includes an extraction module thatuses the set of the generalized extraction patterns to match string inthe information source with the generalized extraction patterns.

Yet another aspect of the present invention is a method of generatingpatterns for use in extracting information from an information source.The method includes establishing a set of strings including at least twoelements corresponding to a subject. A set of generalized extractionpatterns are generated that correspond to the set of strings. Thegeneralized extraction patterns include at least two elements, words andan indication of a position of at least one optional word.

Another method of generating patterns for use in extracting informationfrom an information source relates to the present invention. The methodestablishes a set of strings including at least two elementscorresponding to a subject and identifying consecutive patterns withinthe set of strings that include words and the at least two elements. Aset of generalized extraction patterns is generated from the consecutivepatterns identified. The generalized extraction patterns include the atleast two elements, words and wildcards. The wildcards express acombination of the consecutive patterns.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram of an exemplary computing system environment.

FIG. 2 is a flow diagram of information extraction.

FIG. 3 is a flow diagram for generating and ranking patterns forinformation extraction.

FIG. 4 is a method for generating and ranking generalized extractionpatterns.

FIG. 5 is a method for generating generalized extraction patterns.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

The present invention relates to information extraction. Although hereindescribed with reference to development of patterns for informationextraction, the present invention may also be applied to other types ofinformation processing. Prior to discussing the present invention ingreater detail, one embodiment of an illustrative environment in whichthe present invention can be used will be discussed.

FIG. 1 illustrates an example of a suitable computing system environment100 on which the invention may be implemented. The computing systemenvironment 100 is only one example of a suitable computing environmentand is not intended to suggest any limitation as to the scope of use orfunctionality of the invention. Neither should the computing environment100 be interpreted as having any dependency or requirement relating toany one or combination of components illustrated in the exemplaryoperating environment 100.

The invention is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well known computing systems, environments, and/orconfigurations that may be suitable for use with the invention include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, and the like.

The invention may be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a computer. Generally, program modules include routines,programs, objects, components, data structures, etc. that performparticular tasks or implement particular abstract data types. Theinvention may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotecomputer storage media including memory storage devices. Tasks performedby the programs and modules are described below and with the aid offigures. Those skilled in the art can implement the description andfigures as processor executable instructions, which can be written onany form of a computer readable medium.

With reference to FIG. 1, an exemplary system for implementing theinvention includes a general purpose computing device in the form of acomputer 110. Components of computer 110 may include, but are notlimited to, a processing unit 120, a system memory 130, and a system bus121 that couples various system components including the system memoryto the processing unit 120. The system bus 121 may be any of severaltypes of bus structures including a memory bus or memory controller, aperipheral bus, and a local bus using any of a variety of busarchitectures. By way of example, and not limitation, such architecturesinclude Industry Standard Architecture (ISA) bus, Micro ChannelArchitecture (MCA) bus, Enhanced ISA (EISA) bus, Video ElectronicsStandards Association (VESA) local bus, and Peripheral ComponentInterconnect (PCI) bus also known as Mezzanine bus.

Computer 110 typically includes a variety of computer readable media.Computer readable media can be any available medium or media that can beaccessed by computer 110 and includes both volatile and nonvolatilemedia, removable and non-removable media. By way of example, and notlimitation, computer readable media may comprise computer storage mediaand communication media. Computer storage media includes both volatileand nonvolatile, removable and non-removable media implemented in anymethod or technology for storage of information such as computerreadable instructions, data structures, program modules or other data.Computer storage media includes, but is not limited to, RAM, ROM,EEPROM, flash memory or other memory technology, CD-ROM, digitalversatile disks (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devices,or any other medium which can be used to store the desired informationand which can be accessed by computer 110. Communication media typicallyembodies computer readable instructions, data structures, programmodules or other data in a modulated data signal such as a carrier waveor other transport mechanism and includes any information deliverymedia. The term “modulated data signal” means a signal that has one ormore of its characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media includes wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer readable media.

The system memory 130 includes computer storage media in the form ofvolatile and/or nonvolatile memory such as read only memory (ROM) 131and random access memory (RAM) 132. A basic input/output system 133(BIOS), containing the basic routines that help to transfer informationbetween elements within computer 110, such as during start-up, istypically stored in ROM 131. RAM 132 typically contains data and/orprogram modules that are immediately accessible to and/or presentlybeing operated on by processing unit 120. By way of example, and notlimitation, FIG. 1 illustrates operating system 134, applicationprograms 135, other program modules 136, and program data 137.

The computer 110 may also include other removable/non-removablevolatile/nonvolatile computer storage media. By way of example only,FIG. 1 illustrates a hard disk drive 141 that reads from or writes tonon-removable, nonvolatile magnetic media, a magnetic disk drive 151that reads from or writes to a removable, nonvolatile magnetic disk 152,and an optical disk drive 155 that reads from or writes to a removable,nonvolatile optical disk 156 such as a CD ROM or other optical media.Other removable/non-removable, volatile/nonvolatile computer storagemedia that can be used in the exemplary operating environment include,but are not limited to, magnetic tape cassettes, flash memory cards,digital versatile disks, digital video tape, solid state RAM, solidstate ROM, and the like. The hard disk drive 141 is typically connectedto the system bus 121 through a non-removable memory interface such asinterface 140, and magnetic disk drive 151 and optical disk drive 155are typically connected to the system bus 121 by a removable memoryinterface, such as interface 150.

The drives and their associated computer storage media discussed aboveand illustrated in FIG. 1, provide storage of computer readableinstructions, data structures, program modules and other data for thecomputer 110. In FIG. 1, for example, hard disk drive 141 is illustratedas storing operating system 144, application programs 145, other programmodules 146, and program data 147. Note that these components can eitherbe the same as or different from operating system 134, applicationprograms 135, other program modules 136, and program data 137. Operatingsystem 144, application programs 145, other program modules 146, andprogram data 147 are given different numbers here to illustrate that, ata minimum, they are different copies.

A user may enter commands and information into the computer 110 throughinput devices such as a keyboard 162, a microphone 163, and a pointingdevice 161, such as a mouse, trackball or touch pad. Other input devices(not shown) may include a joystick, game pad, satellite dish, scanner,or the like. These and other input devices are often connected to theprocessing unit 120 through a user input interface 160 that is coupledto the system bus, but may be connected by other interface and busstructures, such as a parallel port, game port or a universal serial bus(USB). A monitor 191 or other type of display device is also connectedto the system bus 121 via an interface, such as a video interface 190.In addition to the monitor, computers may also include other peripheraloutput devices such as speakers 197 and printer 196, which may beconnected through an output peripheral interface 195.

The computer 110 may operate in a networked environment using logicalconnections to one or more remote computers, such as a remote computer180. The remote computer 180 may be a personal computer, a hand-helddevice, a server, a router, a network PC, a peer device or other commonnetwork node, and typically includes many or all of the elementsdescribed above relative to the computer 110. The logical connectionsdepicted in FIG. 1 include a local area network (LAN) 171 and a widearea network (WAN) 173, but may also include other networks. Suchnetworking environments are commonplace in offices, enterprise-widecomputer networks, intranets and the Internet.

When used in a LAN networking environment, the computer 110 is connectedto the LAN 171 through a network interface or adapter 170. When used ina WAN networking environment, the computer 110 typically includes amodem 172 or other means for establishing communications over the WAN173, such as the Internet. The modem 172, which may be internal orexternal, may be connected to the system bus 121 via the user-inputinterface 160, or other appropriate mechanism. In a networkedenvironment, program modules depicted relative to the computer 110, orportions thereof, may be stored in the remote memory storage device. Byway of example, and not limitation, FIG. 1 illustrates remoteapplication programs 185 as residing on remote computer 180. It will beappreciated that the network connections shown are exemplary and othermeans of establishing a communications link between the computers may beused.

FIG. 2 illustrates an extraction module 200 that extracts informationfrom a database 202 and provides an output of extracted information 204.As will be discussed below, extraction module 200 operates based onextraction patterns learned from a training or test corpus. Asappreciated by those skilled in the art, extraction module 200 mayinclude the extraction patterns and/or access a data structure havingthe patterns to perform extraction. The extraction patterns matchstrings in database 202 during extraction. In an exemplary embodiment ofthe present invention, the extraction patterns include words, elementsand wildcards generated based on a training corpus. As used herein,strings include a sequence of words and words can be of differentlanguages including English, German, Chinese and Japanese. Elements arevariables containing information related to a particular subject andwildcards are indications that denote that words in a string can beskipped and/or a position for optional words during matching. Database202 can be a variety of different information sources. For example,database 202 may be a collection of documents, news group articles, acollection of customer feedback data, and/or any other type ofinformation and stored on a local system or across a wide area networksuch as the Internet. The information can be in text or other form,including for example speech data that can be converted to text. Theextracted information 204 can be excerpts from a plurality of documentsrelated to a particular subject that may be reviewed or furtherprocessed in order to better analyze data in database 202.

Information extraction is concerned with extracting information relatedto a particular subject. Extracted information can include pairs,triplets, etc. of related elements pertaining to the subject. Forexample, when extracting product release information, the elements caninclude a company element and a product element. If the subject relatesto books, the elements can include a book title and author information.Other related elements can include inventor and invention information,question and answer pairs, etc. In general, one or more of the elementsassociated with a subject can be referred to as an “anchor”, which willtypically signal that the information in an string is associated with aparticular subject. For example, a product can be an anchor in acompany/product pair related to product release information. One aspectof the present invention relates to generating patterns that includeelements for extraction.

FIG. 3 illustrates a flow diagram of various modules for developingpatterns to be used by extraction module 200. The modules include apattern generation module 210 and a pattern ranking module 212. Patterngeneration module 210 develops patterns based on a positive examplecorpus 214. The positive example corpus contains strings of text thatinclude elements related to a subject of information to be extracted.Using the positive examples in corpus 214, consecutive patterns aregenerated by module 210. Additionally, pattern generation module 210 canuse wildcards to express combinations of patterns. As a result, thepattern(s) generated by module 210, which is indicated at 216,represents a combination that includes a generalized string.

Below are example training instances that form part of an exemplarycorpus 214. The instances include company and product elements annotatedwith <company> and <product> tags, respectively. The positive traininginstances in corpus 214 are:

<company> Microsoft Corp. </company> today announced the immediateavailability of <product> Microsoft Internet Explorer Plus </product>,the eagerly awaited retail version of Internet Explorer 4.0.

<company> Microsoft Corp. </company> today announced the availability of<product> Microsoft Visual J++ 6.0 Technology Preview 2</product>, abeta release of the next version of the industry's most widely useddevelopment system for Java.

<company> Microsoft Corp. </company> today announced the immediate, freeavailability of <product> Microsoft Visual InterDev 6.0 Marchpre-release </product>, a preview of the new version of the leadingteam-based Web development system for rapidly building data-driven Webapplications.

Given the positive training instances, consecutive patterns can beidentified that contain the elements related to the subject. Forexample, the following three patterns represent consecutive patternsgenerated from the instances above, where the variables <company> and<product> have replaced specific company and product information:

<company> today announced the immediate availability of <product>,

<company> today announced the availability of <product>,

<company> today announced the immediate, free availability of <product>.

Given these consecutive patterns, a generalized extraction patternexpressing the elements of the consecutive patterns containing awildcard can be developed by module 210 such as:

<company> today announced the {\w+3} availability of <product>.

Here, the wildcard {\w+3} denotes that up to three words can be skippedbetween “the” and “availability”. The generalized extraction patternabove “covers” each of the consecutive patterns, that is eachconsecutive pattern can be expressed in terms of the generalizedextraction pattern. Using the generalized extraction pattern with thewildcard, the product information “Microsoft Office 60 Minute IntranetKit Version 2.0” will be extracted from the following sentence since thepattern allows skipping of the words “immediate worldwide” without theneed for an additional consecutive pattern including the words“immediate worldwide”:

<company> Microsoft Corporation </company> today announced the immediateworldwide availability of Microsoft Office 60 Minute Intranet Kitversion 2.0, downloadable for free (connect-time charges may apply) fromthe Office intranet Web site located athttp://www.mircosoft.com/office/intranet/.

Pattern generation module 210 provides an output of unranked patterns216 generated from corpus 214 that include wildcards to pattern rankingmodule 212 such as described above. Pattern ranking module 212 ranks thepatterns received from pattern generation module 210 using a positiveand negative example corpus 218. A negative example contains one elementin a pair but does not contain a second element, for instance the anchordescribed above. For example, the sentence below is a negative examplebecause it contains company information, but does not include a specificproduct and is not related to a product release:

<company> Microsoft Corp. </company> today announced the availability ofan expanded selection of Web-based training through its independenttraining providers.

The patterns obtained from pattern generation module 210 can be rankedby pattern ranking module 212 using a number of different methods. Inone method, the precision of a particular pattern P can be calculated bydividing the number of correct instances extracted from corpus 218divided by the number of instances extracted from corpus 218 usingpattern P. A pattern with a higher precision value is ranked higher bypattern ranking module 212. Additionally, patterns may be removed if acorresponding pattern matches all the positive instances that acorresponding pattern can match. The pattern having the lower precisionvalue can then be removed.

Ranked patterns 220 form the basis for extraction using extractionmodule 200. Positive and/or negative examples 222 can then be used toevaluate the performance of extraction module 200 in providing correctand useful extracted information 204. During extraction, patterns thatrank higher can be used first to match strings in database 202. In oneembodiment, matching is performed in a left-to-right order. For example,in the pattern “x\w+y\w+”, occurrences of x are matched and then anyoccurrences of y are matched.

FIG. 4 illustrates a method 250 for generating and ranking patterns tobe used by extraction module 200. Method 250 is based on what is knownas the Apriori Algorithm. The Apriori Algorithm is founded on the basisthat subsets and associated supersets share similar attributes and acombination of subsets and supersets can be expressed to encompasscharacteristics of both the subsets and supersets. The followingalgorithm can be used to generate generalized extraction patterns, whichwill be described in more detail below with regard to method 250. In thealgorithm provided below, S is a set of input strings (i.e. positiveexample corpus 214), P₁ are the set of words in S, p₁ is an individualword in P₁. P_(i) and P_((i−1)) are sets of patterns for the i^(th)iteration of the algorithm and p_(i) and p_((i−1)) represent patternswithin the i^(th) set. Learn Generalized Extraction Patterns withConstraints Algorithm 1.  S = set of positive example input strings, 2. P₁ = set of words in S; 3.  for (i=2;i≦k;i++){ 4.   P_(i)=find-generalized-extraction-patterns (P_((i−1)),P₁); 5.    foreach (p∈P_(i)){ 6.      if ( not satisfy-constraints(p) ) 7.       remove p from P_(i); 8.      if (p′ s frequency is not largerthan a    threshold) 9.        removep fromP_(i); 10.     if (pdoes notcontain <anchor>) 11.         removep fromP_(i); 12.     } 13.     if (P_(i) is empty ) 14.       Goto line 16; 15.   } 16.   output P=U^(i)_(j=2)P_(j);

Method 250 begins at step 252, where a set of input strings isestablished. The set of input strings is the positive example corpus 214in FIG. 3. The set of input strings includes patterns, in the case of apair of elements, where both portions of a desired pair of informationelements are included. After the set of input strings is established,generalized extraction patterns including wildcards are generated atstep 254. Generating the generalized extraction pattern (which is alsothe sub-algorithm find-generalized-extraction-patterns() in thealgorithm above) is discussed in further detail below with regard toFIG. 5. The generalized extraction patterns include words and elementsin addition to the wildcards that denote other words may appear withinthe pattern.

The generalized extraction patterns can then be evaluated to determinewhether or not they represent reliable candidates for extraction. Atstep 256, patterns that do not satisfy constraints are removed. A numberof different constraints can be used to remove generalized extractionpatterns generated by pattern generation module 210. One constraint isreferred to as a “boundary constraint” wherein a wildcard cannotimmediately be positioned before or after an anchor. This constrainthelps eliminate patterns for which it is difficult to determine wherethe anchor information begins and ends. For example, the followinggeneralized extraction pattern would be removed:

<company> today announced the immediate availability {\w+3} <product>

The above generalized extraction pattern could inappropriately determinethat the string “of Internet Explorer for no-charge download from theInternet” was a product for the following sentence:

Microsoft Corp. today announced the immediate availability of InternetExplorer for no-charge download from the Internet.

Another constraint is the “distant constraint”. The distant constraintlimits the number of words that can be skipped by a wildcard to not belarger than the largest number of words that are skipped based on thetraining data. For example, the following pattern that does not limitthe amount of words to be skipped would not be used:

<company> {\w+} today announced {\w+} deliver <product>.

The above pattern could incorrectly extract “enterprise andelectronic-commerce solutions based on the Microsoft Windows NT Serveroperating system and the BackOffice family of products” as productinformation for the sentence:

Microsoft Corp. and Policy Management Systems Corp. (PMSC) todayannounced a plan in which the two companies will work together todeliver enterprise and electronic-commerce solutions based on theMicrosoft Windows NT Server operating system and the BackOffice familyof products.

Another constraint, called the “island constraint” prohibits what isreferred to as an “isolated function word”. Isolated function words aregenerally articles such as “the”, ‘a’, and “an” that do not includespecific content related to information to be extracted and aresurrounded by wildcards. The following pattern does not satisfy theisland constraint:

<company> {\w+8} the {\w+13} of the <product>, the first

The above pattern could incorrectly extract “Microsoft EntertainmentPack for the Windows CE operating system” as product information that isnot related to a release for the following sentence:

Microsoft Corp. today provided attendees of the Consumer ElectronicsShow in Las Vegas with a demonstration of the Microsoft EntertainmentPack for the Windows CE operating system, the first game product to bereleased for the Windows CE-based handheld PC platform.

At step 258, patterns that do not meet a frequency threshold areremoved. As a result, patterns that are not commonly used are removed atthis step. At step 260, patterns that do not contain an anchor areremoved. For example, a pattern not containing a product with anassociated company name is not included as a pattern for informationextraction. Given these patterns, the patterns are ranked at step 262.As discussed above, many different ranking methods can be used to rankthe patterns. If patterns rank too low, they can be removed.

FIG. 5 illustrates method 280 for generating generalized extractionpatterns. The algorithm below can be used to generate these patterns,and is a sub-algorithm for the algorithm described above. The samevariables apply to the algorithm below.find-generalized-extraction-pattern(P_((i−1)),P₁) 1.  for each(p_((i−1))∈P_((i−1))){ 2.    for each (p₁∈P₁){ 3.    p_(i)=p_((i−1))p₁;4.     if (p_(i) exists in S) 5.      put p_(i) into P_(i) ; 6.   p′_(i)=p_((i−1)){\w+n}p₁; 7.     if (p′_(i) exists in S) 8.      putp′_(i) into P_(i); 9.    } 10.   } 11.   output P_(i);

At step 282 of method 280, consecutive patterns are identified from thepositive instances in positive example corpus 214. This step correspondsto lines 3 through 5 in the sub-algorithm above. The consecutivepatterns include the elements related to the subject to be extracted,for example company and product. In one method, patterns can berecursively generated given the input strings by combining subsets andsupersets of the strings sharing similar attributes. After theconsecutive patterns have been identified, method 280 proceeds to step284 wherein wildcard positions and lengths are identified by combiningthe consecutive patterns and expressing generalized extraction patternsto cover the consecutive patterns. This step corresponds to lines 6through 8 in the sub-algorithm above. Next, the generalized extractionpatterns with wildcards are output at step 286. The generalizedextraction patterns are then further analyzed as explained above withrespect to method 250 to remove and rank the patterns.

By implementing the present invention described above, generalizedextraction patterns can be developed that represent combinations ofpatterns and provide a more reliable information extraction system. Thegeneralized extraction patterns can include positions for optional wordsand/or wildcards denoting that words can be skipped during matching thatallow combinations of patterns to be expressed. Using the generalizedpatterns during extraction allows for matching of various strings inorder to identify matching strings in an information source.

Although the present invention has been described with reference toparticular embodiments, workers skilled in the art will recognize thatchanges may be made in form and detail without departing from the spiritand scope of the invention.

1. A computer-implemented method of extracting information from aninformation source, comprising: accessing strings in the informationsource; and comparing the strings in the information source withgeneralized extraction patterns and identifying strings in theinformation source that match at least one generalized extractionpattern, the generalized extraction patterns including words andwildcards, wherein the wildcards denote that at least one word in anindividual string can be skipped in order to match the individual stringto an individual generalized extraction pattern.
 2. Thecomputer-implemented method of claim 1 and further comprising extractingat least two elements from strings in the information source that havebeen identified to match, the at least two elements being based on atleast two corresponding elements in a corresponding generalizedextraction patterns.
 3. The computer-implemented method of claim 2wherein for at least one of the corresponding elements in each of thegeneralized extraction patterns, there is at least one word positionedbetween said at least one of the corresponding elements and thewildcards.
 4. The computer-implemented method of claim 1 wherein thewildcards indicate the number of words that can be skipped.
 5. Acomputer-readable medium for extracting information from an informationsource, comprising: a data structure including a set of generalizedextraction patterns including words and an indication of a position forat least one optional word; and an extraction module using the set ofgeneralized extraction patterns to match strings in the informationsource with the generalized extraction patterns.
 6. Thecomputer-readable medium of claim 5 wherein the generalized extractionpatterns further include at least two elements related to a subject. 7.The computer-readable medium of claim 6 wherein for the generalizedextraction patterns there is at least one word positioned between atleast one of the elements and the indication.
 8. The computer-readablemedium of claim 5 wherein the indication includes a number of words thatcan be skipped during information extraction.
 9. A method of generatingpatterns for use in extracting information from an information source,comprising: establishing a set of strings including at least twoelements corresponding to a subject; generating a set of generalizedextraction patterns that correspond to the set of strings, thegeneralized extraction patterns including the at least two elements,words and an indication of a position for at least one optional word.10. The method of claim 9 and further comprising removing patterns fromthe set of generalized extraction patterns that do not meet a frequencythreshold in the set of strings.
 11. The method of claim 9 and furthercomprising removing patterns from the set of generalized extractionpatterns that contain the indication adjacent to one of the at least twoelements in the generalized extraction pattern.
 12. The method of claim9 and further comprising removing patterns from the set of generalizedextraction patterns where the number of words to be skipped by theindication is above a threshold.
 13. The method of claim 9 and furthercomprising ranking the generalized extraction patterns in the set ofgeneralized extraction patterns.
 14. The method of claim 13 wherein thestep of ranking further comprises calculating a precision score for eachgeneralized extraction pattern.
 15. The method of claim 13 and furthercomprising removing patterns from the set of generalized extractionpatterns that do not meet a ranking threshold.
 16. The method of claim 9and further comprising determining a number of words that a particularindication will skip.
 17. A method of generating patterns for use inextracting information from an information source, comprising:establishing a set of strings including at least two elementscorresponding to a subject; identifying consecutive patterns within theset of strings that include words and the at least two elements; andgenerating a set of generalized extraction patterns from the consecutivepatterns identified, the generalized extraction patterns including theat least two elements, words and wildcards, wherein the wildcardsexpress a combination of the consecutive patterns.
 18. The method ofclaim 17 and further comprising removing patterns from the set ofgeneralized extraction patterns that do not meet a frequency thresholdin the set of strings.
 19. The method of claim 17 and further comprisingremoving patterns from the set of generalized extraction patterns thatcontain a wildcard adjacent to one of the at least two elements in thegeneralized extraction pattern.
 20. The method of claim 17 and furthercomprising removing patterns from the set of generalized extractionpatterns where the number of words to be skipped by a wildcard is abovea threshold.
 21. The method of claim 17 and further comprising rankingthe generalized extraction patterns in the set of generalized extractionpatterns.
 22. The method of claim 21 wherein the step of ranking furthercomprises calculating a precision score for each generalized extractionpattern.
 23. The method of claim 21 and further comprising removingpatterns from the set of generalized extraction patterns that do notmeet a ranking threshold.
 24. The method of claim 17 and furthercomprising determining a number of words that a particular wildcard willskip.